發(fā)布時間：2018-06-05 06:59

  本文選題：小波變換 + 異構(gòu)并行計算��； 參考：《西安電子科技大學(xué)》2013年碩士論文

【摘要】：目前，隨著計算機硬件技術(shù)的飛速發(fā)展，多核中央處理器(Central ProcessingUnit, CPU)和通用計算的圖形處理器(Graphic Processing Unit, GPU)更新?lián)Q代層出不窮，但軟件應(yīng)用卻跟不上其硬件的發(fā)展速度，導(dǎo)致大量的硬件資源浪費。為了發(fā)揮其各自的性能特點和提高資源的利用率，通過并行計算對密集型計算任務(wù)加速，具有很高的應(yīng)用價值和實際意義。 小波變換以其在時域和頻域都具有良好的局部化特性使得其在圖像壓縮編碼領(lǐng)域得到廣泛的研究和應(yīng)用。而隨著圖像信息數(shù)量的劇增，，對小波變換的加速處理進而提升圖像壓縮的速度具有重要的實際意義。 本文詳細分析了小波變換的原理及過程，并闡述了CPU-GPU異構(gòu)并行計算的特點。在CPU實現(xiàn)小波變換基礎(chǔ)上，研究了CPU-GPU異構(gòu)并行計算的程序任務(wù)的劃分，性能的評估以及性能優(yōu)化策略。通過對小波變換實現(xiàn)的具體分析和研究，采用了基于OpenMP-CUDA混合編程模型實現(xiàn)了異構(gòu)系統(tǒng)的協(xié)同運算。重點研究了9/7小波濾波在GPU上的優(yōu)化，通過對小波變換算法的優(yōu)化，使其更適應(yīng)GPU上硬件資源結(jié)構(gòu)和更能發(fā)揮并行計算性能上的優(yōu)勢。針對小波濾波中數(shù)據(jù)劃分帶來低通和高通系數(shù)相關(guān)性問題，提出在代價最小的情況下實現(xiàn)數(shù)據(jù)的并行劃分，極大提高了程序的運行效率�；贑PU-GPU異構(gòu)并行計算的小波變換相比CPU串行的處理速度提升了15-36倍。
[Abstract]:At present, with the rapid development of computer hardware technology, multi-core CPU Central processing Unit (CPU) and General Computing Graphics processor (GPU) are constantly emerging, but the application of software can not keep up with the development of its hardware. Lead to a lot of waste of hardware resources. In order to give full play to their respective performance characteristics and improve the utilization of resources, intensive computing tasks are accelerated through parallel computing. Wavelet transform has been widely studied and applied in the field of image compression because of its good localization in time domain and frequency domain. With the rapid increase of the number of image information, it is of great practical significance to accelerate the processing of wavelet transform and then accelerate the speed of image compression. In this paper, the principle and process of wavelet transform are analyzed in detail, and the characteristics of CPU-GPU heterogeneous parallel computing are expounded. Based on the wavelet transform implemented by CPU, the task partition, performance evaluation and performance optimization strategy of CPU-GPU heterogeneous parallel computing are studied. By analyzing and studying the realization of wavelet transform, a hybrid programming model based on OpenMP-CUDA is used to realize the cooperative operation of heterogeneous systems. This paper focuses on the optimization of 9 / 7 wavelet filtering on GPU. By optimizing the wavelet transform algorithm, it is more suitable for the hardware resource structure on GPU and can play the advantage of parallel computing performance. In order to solve the problem of correlation between low pass and high pass coefficients brought by data partition in wavelet filtering, the parallel partition of data is proposed in the case of minimum cost, which greatly improves the running efficiency of the program. The processing speed of wavelet transform based on CPU-GPU isomerous parallel computing is 15-36 times faster than that of CPU.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP338.6;TP391.41

【參考文獻】

相關(guān)會議論文 前1條

1 袁林;張國峰;戴樹嶺;;基于小波變換的圖像壓縮算法研究[A];第五屆全國仿真器學(xué)術(shù)會論文集[C];2004年

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